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Probabilistic Graphical Model
This page contains resources about Probabilistic Graphical Models. Subfields See Category:Machine Learning for some of its subfields. * Bayesian Machine Learning and Bayesian Networks (directed graphical models) ** * Markov Random Fields (undirected graphical models) ** Potts model *** Ising model ** Hopfield network ** Boltzmann Machine *** Restricted Boltzmann Machine **Gaussian MRF / Undirected Gaussian Graphical Model Online Courses Video Lectures * Probabilistic Graphical Models by Daphne Koller * Machine Learning, Probability and Graphical Models by Sam Roweis - VideoLectures.Net * Graphical Models by Zoubin Ghahramani - VideoLectures.Net * Graphical Models by Cedric Archambeau - VideoLectures.Net * Introduction to Graphical Models for Data Mining by Arindam Banerjee - VideoLectures.Net * Bayesian Learning by Zoubin Ghahramani - VideoLectures.Net * Graphical modelling and Bayesian structural learning by Peter Green - VideoLectures.Net *Graphical Models by Christian Borgelt *Learning Bayesian Networks by Richard E. Neapolitan - VideoLectures.Net *Machine Learning, Probability and Graphical Models by Sam Roweis - VideoLectures.Net *Probabilistic Graphical Models by Sam Roweis - VideoLectures.Net Lecture Notes * Probabilistic Graphical Models by Sargur Srihari * Probabilistic Graphical Models by David Sontag * Probabilistic Graphical Models by Andreas Krause * Probabilistic Graphical Models by Eric Xing * Probabilistic Graphical Models Course by Sargur Srihari * Foundations of Graphical Models by David M. Blei * Probabilistic Models of Discrete Data by David M. Blei * CS228: Probabilistic Graphical Models by Stefano Ermon * CSC 2541: Topics in Machine Learning: Bayesian Methods for Machine Learning by Radford Neal * CSE 515T: Bayesian Methods in Machine Learning by Roman Garnett * CS 281A/Stat 241A: Statistical Learning Theory - Probabilistic Graphical Models by Michael Jordan Books and Book Chapters * Bellot, D. (2016). Learning Probabilistic Graphical Models in R. Packt Publishing. * Koduvely, H. M. (2015). Learning Bayesian Models with R. ''Packt Publishing. * Hastie, T., Tibshirani, R., & Wainwright, M. (2015). "Chapter 9: Graphs and Model Selection". ''Statistical learning with sparsity: the lasso and generalizations. CRC Press. * Davidson-Pilon, C. (2015). Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional. * Ankan, A., & Panda, A. (2015). Mastering Probabilistic Graphical Models Using Python. Packt Publishing Ltd. * Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press. * Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press. * Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian Networks in R. Springer, 122, 125-127. * Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models. MIT Press. * Darwiche, A. (2009). Modeling and reasoning with Bayesian networks. Cambridge University Press. * Borgelt, C., Steinbrecher, M., & Kruse, R. R. (2009). Graphical Models - Representations for Learning, Reasoning and Data Mining. John Wiley & Sons. * Wainwright, M. J., & Jordan, M. I. (2008). Graphical models, exponential families, and variational inference. Foundations and Trends® in Machine Learning, 1''(1-2), 1-305. * Bishop, C. M. (2006). "Chapter 8. Graphical Models". ''Pattern Recognition and Machine Learning. Springer. pp. 359–422. * Jordan, M. I. (2003). An Introduction to Probabilistic Graphical Models. * Cowell, R. G., D., A. Philip, L., Steffen L., & Spiegelhalter, D. J. (1999). Probabilistic Networks and Expert Systems. Springer. * Lauritzen, S. L. (1996). Graphical Models. Oxford University Press. * Jensen, F. (1996). An Introduction to Bayesian Networks. Springer. * Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann. Scholarly Articles * Airoldi, E. M. (2007). Getting Started in Probabilistic Graphical Models. PLoS Computational Biology, 3(12), e252. * Wainwright, M. J., & Jordan, M. I. (2008). Graphical Models, Exponential Families, and Variational Inference. Foundations and Trends® in Machine Learning, 1(1-2), 1-305. * Jordan, M. I. (2004). Graphical Models. Statistical Science, 140-155. Tutorials * Heckerman's Bayes Net Learning Tutorial * A Brief Introduction to Graphical Models and Bayesian Networks by Kevin Murphy * An Introduction to Graphical Models - Tutorial by Michael Jordan * Bayesian Modelling in Machine Learning: A Tutorial Review * Bayesian Methods for Machine Learning - Tutorial in NIPS 2004 Software *PRMLT - MATLAB Toolbox for the book of PRML by C. Bishop * pmtk3 - Probabilistic Modeling Toolkit for MLPP book by Murphy in Matlab/Octave (3rd edition) * pyprobml - Python code for MLPP book by K. Murphy * BRMLtoolbox - MATLAB and Julia code for the BRML book by D. Barber * PyBRML - Python code for the BRML book by D. Barber * Bayesian Probabilistic Matrix Factorization - MATLAB *Mens X Machina PGM Toolbox - MATLAB *UGM (undirected graphical models) - MATLAB *Module libpgm - Python *Graphical Models Toolkit (GMTK) *Bayesian Modeling and Monte Carlo Methods * SamIam * BNT - Bayes Net Toolbox in MATLAB * libDAI - C++ * OpenGM - C++ * Stan - C++ and R * PyMC3 - Python * Infer.NET - Developed by Microsoft Research * OpenBUGS - Bayesian Inference Using Gibbs Sampling See also * Monte Carlo Methods * Stochastic Processes and Random Fields Other Resources *Comparison of software toolkits *Probabilistic Graphical Models wiki *Bayesian machine learning - Metacademy *Bayesian machine learning - Introduction *Bayesian machine learning - FastML Category:Probability and Statistics Category:Machine Learning Category:Probabilistic Graphical Models